The Division of Intramural Population Health Research (DIPHR) is an intramural research division at the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) whose mission is to improve population health across the life course by maximizing health and preventing disease [http://www.nichd.nih.gov/about/org/DIPHR/Pages/default.aspx]. The Division comprises the Office of the Director along with three research branches: Biostatistics and Bioinformatics, Epidemiology, and Health Behavior. As an intramural research entity, the Division designs and conducts innovative, high-risk research aimed at answering critical data gaps regarding human fecundity, reproduction and development; pregnancy and delivery, including adverse pregnancy outcomes; offspring health, growth and development from birth to adulthood, and adolescent behavior. In addition, the Division conducts original methodologic research aimed at developing novel statistical tools for analytic purposes. The Division's research focuses on various population subgroups including couples or individuals of reproductive age, pregnant women, infants, children, and adolescents. Increasingly, research is designed to assess the early origins of health and disease underscoring the need for the development of accurate methods for measuring environmental (non-genetic) exposures during sensitive windows of human development in the context of genetics, with the overarching goal of identifying population level interventions for maximizing health across the lifespan. The multi-faceted nature of the Division's research includes normative, etiologic and interventional research, examples which follow: 1) studies aimed at defining 'normal' or recruiting generally healthy participants include the NICHD Fetal Growth Studies and BioCycle Study, and 2) studies aimed at determining the relation between environmental (non-genetic) exposures (e.g., Upstate KIDS Study, ENDO Study and LIFE Study), 3) bio-behaviors (e.g., Naturalistic Teen Driving Studies and NEXT Study), and 4) genes (e.g., Diabetes and Women's Health and Genetic Determinants of Birth Defects) and a spectrum of outcomes. In addition, the Division conducts interventional research involving but not restricted to families (e.g., CHEF Trial and FMOD Trial) and clinical populations (e.g., EAGeR Trial and FAZST Trial). The Division also conducts perinatal epidemiologic studies involving detailed collection of information from medical records (e.g. Consortium on Safe Labor and NICHD Consecutive Pregnancies Study) Innovative and flexible research sites capable of implementing epidemiologic and behavioral research focusing on human reproduction; pregnancy; infant, child and adolescent health, growth and development; and human behavior are needed to assist the Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), in the successful completion of original population health research. The breadth and complexity of population health research undertaken by Division investigators poses unique challenges with regard to several aspects: defining sampling frameworks that can be enumerated, incorporating varying units of analysis (e.g., individuals, menstrual or treatment cycles, couples, twins/siblings, families) into study designs; specifically targeted recruitment and retention of study participants; and longitudinal questionnaire data and biospecimen collection to capture of time-varying covariates and outcomes. Of note is the hierarchical data structure generated by much of the Division's research reflecting the recruitment of triads/dyads or genome wide analytic studies (GWAS) or multi-scale data from study participants (e.g., day, cycle, woman and couple level data collection for fecundity and fertility research, schools, census levels). Examples of past sampling frameworks utilized by the Division typically include population-based strategies (e.g., random samples of well-defined registries, marketing databases), clinically based sampling (e.g., administrative and clinic records, surgical schedules), schools, and convenience-based sampling with strict eligibility criteria for trials from clinical sites, schools and the general public. Increasingly, the Division recognizes the utility of leveraging existing cohorts for life course approaches to health and disease (e.g., Diabetes &Women's Health study), and continues to explore new avenues for sampling population subgroups including the use of social media and web-based venues. Irrespective of the final sampling framework, the Division strives to design research and implement sampling strategies that characterize their respective referent populations to maximize the eventual translation of findings. Division investigators are responsible for designing original research to answer critical and pressing data gaps within the mission. Investigators in the Biostatistics and Bioinformatics Branch lead the development of the analytic plan for Division research. Thus, the Division's research is innovative in design with the upside of positively impacting population health through the translation of findings. In the next decade, the Division is planning to develop and maintain new cohorts for life course and intergenerational epidemiologic and behavioral research. This strategy may include the leveraging of existing cohorts and the development of unique population subgroups, such as couples at risk for pregnancy or actively trying to become pregnant, couples undergoing infertility treatment and assisted reproductive technologies, pregnant women with underlying health conditions such as asthma or with pregnancy complications, families with children with developmental disabilities, overweight or obese pregnant women or uniquely exposed geographic cohorts from diverse backgrounds and socioeconomic standing. A component of the life course approach is the Division's plan to encompass the exposome paradigm, or the measurement of the totality of environmental exposures across the lifespan that can be analyzed against the background of the genome. To this end, the Division foresees the design and implementation of high dimensional etiologic research, from the genome to the proteome, transcriptome, and metabolome as we seek to define the humanome. Relatedly, the Division plans to develop prediction models for a spectrum of health and disease outcomes that can be targeted for use by scientific disciplines, clinicians and the public alike.